Topic: latent variable model

Substantial progress towards understanding neural computation has been made by studying the dynamical systems that govern neural population activity. Because dynamical models make predictions about the temporal evolution of neural states, perturbations of neural activity are a powerful tool to evaluate and refine these models. Here, using electrodes and Neuropixels in the motor cortices of macaques engaged in a reaching task, we probed how local population dynamics are affected by two perturbation modalities: optogenetic excitation and electrical intracortical microstimulation (ICMS). Both st

In recent years, advances in the fields of computational ethology and neuroscience have enabled detailed modeling of animal behavior and its neural underpinnings. However, these advances have yet to produce normative models of neuromotor control that generate the full diversity of animal behavior. Using recently developed methods in 3D pose estimation and deep reinforcement learning, we trained a latent variable model to control a virtual rodent body to imitate the natural behaviors of real rats in a physics simulator. Once trained, the model faithfully replicated diverse rat behaviors and gen

Visual cortical neurons are richly inter-connected by within-area, and feedforward and feedback across-area circuits. How these different circuits contribute to visual function remains poorly understood. We performed simultaneous recordings from hundreds of neurons in V1 and V2 in anesthetized macaque monkeys. Using a latent variable model, we identified the feedforward, feedback, and within-area signals that contributed to the measured responses. The identified feedforward and feedback signals contributed equally to explaining activity in the two areas, consistent with their equal anatomical

Hippocampal studies provide a rich window into the mechanisms of memory via the paradigm of spatial navigation. To interpret neural responses, it is common practice to first build encoding models using position during locomotion as the label, and then decode the position using this model. Of particular interest is the mismatch between the decoded and the actual position, for instance, replay of non-local trajectories during rest. However, it is unclear whether a priori chosen labels explain neural data the best. By imposing spatial structure to times of immobility, we may be systematically mis